A new training scheme for neural network based non-linear channel equalizers in wireless communication system using Cuckoo Search Algorithm

Widespread use of the internet has resulted in a massive rise in the data rate of a wireless communication system. Eventually, to mitigate the effect of inter-symbol interference (ISI) in multipath wireless channels, designing a channel equalizer is becoming more demanding. For severe non-linear distortion, the neural network (NN) based non-linear channel equalizers provide superior performance than the adaptive filter based linear equalizers. The NN equalizers are generally trained with back-propagation algorithm which has limitations of slower convergence, local minima stagnation and sensitivity to initial parameters. To overcome these limitations, this paper proposes a training scheme using Cuckoo Search Algorithm (CSA) for functional link artificial NN (FLANN) based channel equalizers. The proposed training scheme has a better ability to escape from local minima, higher exploitation and exploration capabilities. To choose the optimum values of the parameters, the sensitivity analysis of the CSA based approach is performed with its key parameters. Furthermore, three non-linear channels have been simulated to demonstrate the equalization performance of the CSA based training scheme and the results have been compared with recent and well-established algorithms. The simulations confirm that the proposed training scheme performs substantially better than existing metaheuristic algorithms in terms of BER and MSE performance. To show the robustness of the CSA based method, the burst error scenario has been considered and results proved that the method is more successful in handling such scenarios when compared to other methods. The performance of the proposed scheme has been validated for a wide range of signal-to-noise ratio through simulation studies and it is observed that the scheme outperforms the other algorithms in poor SNR conditions as well. Also, to examine the statistical significance of the results provided by the proposed scheme, the Wilcoxon test is performed and the test reveals that the obtained results are statistically significant. (C) 2020 Published by Elsevier GmbH.

Automated summary

This study proposes a training scheme for FLANN channel equalizers based on CSA method, which can optimize channel equalization performance better and show significant advantages in simulation results. The scheme performs significantly well in handling burst error scenarios and maintains good performance even under poor signal-to-noise ratio conditions.